1. Field of the Invention
The present invention generally relates to predictive modeling for optimizing a business objective and, more particularly, to presentation of feature importance information available in a predictive model with correlation information among the variables.
2. Background Description
There is an increasing interest in the use of predictive modeling as a means to provide information on levers, drivers, and/or triggers to control in order to optimize some objective in a business optimization problem. For example, in trigger-based marketing, profit modeling provides key drivers in terms of customer behavior and marketing actions. In customer lifetime value management, lifetime value modeling provides key drivers in terms of customer behavior and marketing actions. In price optimization, demand forecasting provides key drivers in terms of pricing strategy, product features, etc. In corporate performance management, performance indicator modeling provides key drivers in terms of operational performance metrics and on-demand indices.
There are, however, problems with the use of predictive modeling as a means to provide feature importance. In many real world applications of predictive modeling, the user is interested in finding out features that are key “drivers” or “triggers” that are likely to affect the outcome (or the target). Current practice is to present feature information available in the estimated models (e.g., regression coefficients in linear regression) as importance measures. There is a problem with this approach, because in predictive modeling important features can be shadowed by other features that are highly correlated with them and consequently receive low importance measures. Another problem is that some features having high importance may not be causally related to the target variable, or not easily controllable, and alternatives may need to be sought.
Hence, methods are needed that augment typical feature importance information given by a predictive model to facilitate more flexible choices of actions.